article · Sustainability
Nano zero-valent aluminium was evaluated as an adsorbent to eliminate copper ions from aqueous solutions, with machine learning models developed to predict removal efficiency based on adsorption factors. Physical and chemical characterisation confirmed the elemental composition, surface morphology, and texture of the nanomaterial. Under test conditions of 50 milligrams per litre initial copper concentration, 1.0 gram per litre adsorbent dose, pH 5, and 30 degrees Celsius at 150 revolutions per minute, the material achieved a removal efficiency of 53.2 percent within 10 minutes. The adsorption behaviour aligned closely with the Langmuir isotherm and pseudo-second-order kinetic models. To predict removal efficiency, artificial neural networks, support vector regression, and linear regression were evaluated. The artificial neural network model delivered the highest accuracy, exhibiting a mean squared error of less than 10 to the power of minus five.
Removing toxic heavy metals such as copper from contaminated water is critical for environmental protection and public health. Utilising nanomaterials offers a rapid method for capturing pollutants, while computational tools like machine learning can accurately model and forecast treatment outcomes. This reduces the need for extensive trial-and-error laboratory experiments when determining optimal operational parameters.
The findings could inform the design of water treatment systems that utilise nano zero-valent aluminium to eliminate heavy metal pollutants. Potential users include municipal water utilities and industrial wastewater treatment operators seeking to optimise treatment parameters computationally. However, the study represents early-stage laboratory research, meaning further validation and engineering development are required before practical deployment at industrial scale.
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Predicting the heavy metals adsorption performance from contaminated water is a major environment-associated topic, demanding information on different machine learning and artificial intelligence techniques. In this research, nano zero-valent aluminum (nZVAl) was tested to eliminate Cu(II) ions from aqueous solutions, modeling and predicting the Cu(II) removal efficiency (R%) using the adsorption factors. The prepared nZVAl was characterized for elemental composition and surface morphology and texture. It was depicted that, at an initial Cu(II) level (Co) 50 mg/L, nZVAl dose 1.0 g/L, pH 5, mixing speed 150 rpm, and 30 °C, the R% was 53.2 ± 2.4% within 10 min. The adsorption data were well defined by the Langmuir isotherm model (R2: 0.925) and pseudo-second-order (PSO) kinetic model (R2: 0.9957). The best modeling technique used to predict R% was artificial neural network (ANN), followed by support vector regression (SVR) and linear regression (LR). The high accuracy of ANN, with MSE < 10−5, suggested its applicability to maximize the nZVAl performance for removing Cu(II) from contaminated water at large scale and under different operational conditions.
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DOI: 10.3390/su15032081
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